High Content Image Cytometry in the Context of Subnuclear Organization

The organization of proteins in space and time is essential to their function. To accurately quantify subcellular protein characteristics in a population of cells with regard for the stochasticity of events in a natural context, there is a fast-growing need for image-based cytometry. Simultaneously, the massive amount of data that is generated by image-cytometric analyses, calls for tools that enable pattern recognition and automated classification. In this article, we present a general approach for multivariate phenotypic profiling of individual cell nuclei and quantification of subnuclear spots using automated fluorescence mosaic microscopy, optimized image processing tools, and supervised classification. We demonstrate the efficiency of our analysis by determination of differential DNA damage repair patterns in response to genotoxic stress and radiation, and we show the potential of data mining in pinpointing specific phenotypes after transient transfection. The presented approach allowed for systematic analysis of subnuclear features in large image data sets and accurate classification of phenotypes at the level of the single cell. Consequently, this type of nuclear fingerprinting shows potential for high-throughput applications, such as functional protein assays or drug compound screening.